Landsat Soil Adjusted Vegetation Index (SAVI) is used to correct Normalized Difference Vegetation Index (NDVI) for the influence of soil brightness in areas where vegetative cover is low.

2023年7月27日 · Soil Adjusted Vegetation Index (SAVI) provides information on vegetation growth and productivity while correcting for influences of soil reflectance. Effects due to climate, weather, fire, water, and vegetation and crop management can be assessed through developing and visualizing maps, time series, and anomalies of SAVI.

2021年7月8日 · 这段代码展示了如何从LANDSAT 7影像集中筛选一个月的数据，并运用数学表达式计算NDVI（归一化植被指数）和SAVI（土壤调整植被指数）。 通过映射函数到影像集合，然后求平均值，最终在地图上显示了这两个指数的平均值。 这种方法有助于分析植被覆盖和土壤条件。 本例是将表达式映射到集合上，同时也是利用指数表达式计算SAVI和NDVI的值。 var collection = ee. ImageCollection ('LANDSAT/LE07/C01/T1_TOA') . filterDate ('2002-11-01', '2002-12-01'); });

In an attempt to improve NDVI, Huete [1] developed a vegetation index that accounted for the differential red and near-infrared extinction through the vegetation canopy. The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths.

2018年12月5日 · The Soil-Adjusted Vegetation Index (SAVI) is a vegetation index that attempts to minimize soil brightness influences using a soil-brightness correction factor. This is often used in arid regions where vegetative cover is low.

\[SAVI = \frac{B8-B4}{B8+B4+L} \cdot (1+L)\] where $L$ is the soil brightness correction factor and could range from (0 -1). Empirically derived NDVI products have been shown to be unstable, varying with soil colour, soil moisture, and saturation effects from high density vegetation.

Landsat Surface Reflectance-derived Modified Soil Adjusted Vegetation Index (MSAVI) are produced from Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8-9* Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) Collection 1 and Collection 2 scenes that have been processed to Landsat Level-2 Surf...

The SAVI results from the following equation: SAVI = (1 + L) * (IR_factor * near_IR - red_factor * red) / (IR_factor * near_IR + red_factor * red + L) where: L is a correction factor which ranges from 0 for very high vegetation cover to 1 for very low vegetation cover.

2024年4月23日 · 本文将探索SAVI的意义、计算方法以及在不同领域的应用。 1. 什么是SAVI？ SAVI是对NDVI的改进，旨在减少土壤表面反射对 植被指数 的影响，提高其在土壤裸露或低植被覆盖地区的准确性。 SAVI的计算公式如下： SAVI= (NIR+Red+L) (NIR−Red)× (1+L) 其中，NIR代表近红外波段的反射率，Red代表红光波段的反射率，L是一个调整因子，通常取值在0到1之间，用于减少土壤表面反射的影响。 2. SAVI的应用. 农业生产：SAVI在农业生产中发挥着重要作用 …

SAVI is relatively easy to calculate and can be done with any remote sensing package (e.g., ERDAS Imaging, ENVI, Idrisi) or with a GIS program that can handle raster processing (e.g., ArcGIS with Spatial Analyst extension, GRASS). Sample Graphic. NDVI and SAVI calculated from a Landsat TM5 image of southwestern Owyhee County, Idaho.